class_docstr: Callback that prints metrics to stdout.
  
  Arguments:
      count_mode: One of "steps" or "samples".
          Whether the progress bar should
          count samples seen or steps (batches) seen.
      stateful_metrics: Iterable of string names of metrics that
          should *not* be averaged over an epoch.
          Metrics in this list will be logged as-is.
          All others will be averaged over time (e.g. loss, etc).
          If not provided, defaults to the `Model`'s metrics.
  
  Raises:
      ValueError: In case of invalid `count_mode`.
code: def on_train_begin(self, logs=None):
      # When this logger is called inside `fit`, validation is silent.
      self._called_in_fit = True
  
imports: import copy
  import os
  import sys
  import time
  import numpy
  from .callback import Callback
label_desc: Use image node_id 3 for calling the ProgbarLogger obj's underlying member method code with example usage: obj.on_train_begin(logs) without return types